The invention has particular, although not exclusive, application to event or novelty detection in time dependent two or three dimensional imaging data. Particular reference will be made to this application of the invention in the following, but it is to be understood that the invention has broader application.
Imaging data may be recorded representing physical properties of an object at regularly spaced locations in a one-dimensional, two-dimensional or three-dimensional spatial array. The data may be acquired at logically different states. Examples of data acquired at logically different states include: data representing a common object recorded at different times; data representing a common object recorded using different imaging techniques; and data representing plural similar objects.
The data can be acquired by a variety of methods. Examples of such methods include nuclear magnetic resonance (NMR), computer axial tomography (CAT), positron emission tomography (PET), emission computed tomography (ECT), multimodality imaging (MMI), and x-ray scanning methods. Each of these methods produces a two-dimensional array of data values, representing a two-dimensional grid within the object, designated as a slice. By repeating the acquisition procedure over all slices of interest, a three-dimensional array of data values results. Two-dimensional data arrays can be acquired as a single slice using any of the aforementioned methods, or with a digitizing camera. Regardless of the method of acquisition, the data represent physical properties of the object. This invention is not dependent on the method of data acquisition.
It is known to analyze such arrays with the addition of explicit prior knowledge of events that are of interest to identify regions and domains associated with those events. A "region" is a connected set of spatial data points including all points on the boundary of the set. A "domain" is a number of disjoint regions. The known methods only seek to identify the regions and domains of the data associated with given and known events. For example, data representing the human brain performing a task over time can indicate the portions of the brain where events occur that are known or expected to be associated with the performance of the task.
Known methods for the detection of events include image differencing, correlation analysis, and statistical t-maps.
The image differencing method subtracts an aggregate of data representing one logical state from an aggregate of data representing another logical state. Domains where the magnitude of the difference is large may signify a qualitative difference in the data between the two logical states.
The statistical t-map approach is similar to the image differencing method, with the addition that the difference is scaled by the pooled standard deviation to derive a t-value. A statistical probability of the two logical states being different is calculated from the t-value. Domains where the resulting statistical probability value is large may signify a difference in the data between the two logical states.
The drawbacks of subtractive procedures of this sort include:
Some information regarding the two states is required a priori, for example times associated with the different states. This biases the measure towards the user's expectations of which time instances are associated with the two states in question. This information may not be evident. PA1 The subtraction procedure is not readily applicable to data with more than two states, for example certain types of temporal data. PA1 The subtraction procedure does not indicate the nature of the novelty. PA1 Subtraction procedures are sensitive to artifacts in the data. PA1 The nature of the event to be found must be known a priori. Thus, the analysis is based on the operator's expectations. PA1 It is not possible to identify unknown or unexpected events in the data. PA1 Correlation analysis is sensitive to time or other analogous shifts in the behavior of the data. Thus, similar characteristics that are shifted out of phase (i.e. displaced) across the data arrays will have a low correlation. PA1 An independent analysis must be performed for each expected response. PA1 providing a data set including plural data arrays, with each array having plural data points and with corresponding data points in the data arrays having data values that may vary across the arrays; and PA1 clustering the data points into plural clusters according to data value patterns across the arrays. PA1 data storage means for recording the data set; and PA1 clustering calculator means for clustering the corresponding data points into clusters according to the data value patterns across the arrays.
The correlation method correlates a waveform representing an assumed pattern with the data. Domains where the correlation is large may signify a correspondence to the pattern. The disadvantages of this type of analysis include:
The most important limitation of the prior art methods is that they can not detect new and unexpected events (novelties). As an example, consider a task that involves some activity of the human brain, such as moving the fingers of the dominant hand. The task could begin by keeping the hand motionless for an initial time, followed by moving the fingers at a successively greater pace until the end of the task, signified with no finger movement. A set of images of the brain can be recorded during each phase of the task. We wish to determine the domains of the brain that were activated during the finger movements, and how these domains were activated over time. Thus the successful detection of events for this example would identify the domains of the brain activated for the differing degrees of finger movement and the corresponding brain activation pattern in each domain. Successfully detected events could include a pattern which follows the activity of finger movement, and another pattern representing no change in brain activity (a null event).
The prior art methods require explicit knowledge of the events associated with each phase of the finger movement task. The image differencing method requires temporal data in order to subtract the average of data acquired with the hand at rest from the average of data acquired with hand movement. The correlation method requires a knowledge of the waveform representation of the activity associated with finger movement in order perform a correlation analysis with that waveform. The statistical t-map method, like the image differencing method, requires prior knowledge of the event to identify data associated with the hand at rest and with finger movement.
With the known methods the domains where the results are large in magnitude may signify finger movement. However, if the time of each phase of the task is unavailable, then none of the known methods can detect the events. As an example, if the finger movement task is replaced with a cognitive task such as calculating the square root of several prime numbers, then the times associated with the states of the task may be unknown. Also, if the response of a brain region is delayed, the known methods are incapable of detecting the delayed response. The known methods may still assign a relatively large magnitude to the delayed response in comparison to other responses, introducing imprecise and unreliable results. All known methods are also incapable of detecting graduated degrees of brain activity resulting from the increasing pace of finger movement since the actual brain response is unknown. In this case, pairwise analysis is inappropriate since no definite start and stop times can be identified for the graduated task.
The present invention is concerned with a technique for detecting events and novelties in data that does not require prior knowledge or a preconceived notion of the novelty or event and thus can identify entirely unexpected characteristics.
With imaging data, the invention is concerned with the detection of spatial domains associated with events, as well as non-spatial-dimension characteristics of the events. The non-spatial-dimension characteristics may be characteristics in the non-spatial dimension in which the data representation of the object may have logically different states.
Images often contain a large range of intensity values differentiating between physical attributes of an object. Since events can occur at any value of intensity, it would in most cases be desirable to detect similar events independently of the intensity value at the location of the events.